Image enhancement involves one or more processes that are designed to improve the visual appearance of an image. Typical image enhancement processes include sharpening, noise reduction, scene balancing, and tone-correction.
Global or otherwise unguided application of image enhancement processes typically does not produce satisfactory results because the enhancements that are desired by most observers of an image typically vary with image content. For example, it generally is desirable to sharpen image content, such as trees, grass, and other objects containing interesting texture, edge or boundary features, but it is undesirable to sharpen certain features of human faces (e.g., wrinkles and blemishes). Conversely, it typically is desirable to smooth wrinkles and blemishes on human faces, but it is undesirable to smooth other types of image content, such as trees, grass, and other objects containing interesting texture, edge or boundary features.
In an effort to avoid the undesirable effects of global or unguided image enhancement, various manual image enhancement approaches have been developed to enable selective application of image enhancement processes to various image content regions in an image. Some of these approaches rely on manual selection of the image content regions and the desired image enhancement process: Most manual image enhancement systems of this type, however, require a substantial investment of money, time, and effort before they can be used to manually enhance images. Even after a user has become proficient at using a manual image enhancement system, the process of enhancing images is typically time-consuming and labor-intensive.
Automatic image enhancement approaches for avoiding the undesirable effects of global or unguided image enhancement also have been developed. Some of these approaches automatically create maps of local attributes such as noise or sharpness estimations, and gain local control over enhancement parameters based on those maps. Other approaches typically rely on automatic region-based image enhancement. In accordance with one automatic region-based image enhancement approach, the amount of image enhancement is determined automatically for each region in an image based on the subject matter content in the region. In this process, a respective probability distribution is computed for each type of targeted subject matter content across the image. Each probability distribution indicates for each pixel the probability that the pixel belongs to the target subject matter. Each probability distribution is thresholded to identify candidate subject matter regions. Each of the candidate subject matter regions is subjected to a unique characteristics analysis that determines the probability that the region belongs to the target subject matter. The unique characteristics analysis process generates a belief map of detected target subject matter regions and assigns to each of the detected regions an associated probability indicating the probability that the region belongs to the target subject matter. A given belief map only relates to one type of subject matter and the pixels in any given one of the detected subject matter regions in a belief map are assigned the same probability value. Since region detection, as good as it may be, is prone to some errors, the probability discontinuities between the detected subject matter regions and other regions in each belief map necessarily produce artifacts in the resulting enhanced image. For example, if a face region is detected and enhanced using a parameter that is different from the parameter used for the rest of the body, artifacts will most probably be created in border regions. In addition, reality does not always support subject matter regions but rather a continuous change from region to region. This is most evident and most important in terms of avoiding artifacts for image sub-regions requiring drastically different enhancement types, for example, it typically is desirable to smooth facial skin areas and sharpen facial features, such as eyes and lips.
What are needed are image enhancement approaches that avoid artifacts and other undesirable problems that arise as a result of enhancing an image based on segmentations of the image into discrete subject matter regions. Image enhancement systems and methods that are capable of enhancing images in face and skin sensitive ways also are needed.